DeTox：一种预测发育毒性潜力的动物试验替代方法。
DeTox: an Alternative to Animal Testing for Predicting Developmental Toxicity Potential.
作者信息
Tieghi Ricardo Scheufen, Rath Marielle, Moreira-Filho José Teófilo, Wellnitz James, Martin Holli-Joi, Gates Kathleen, Hogberg Helena T, Kleinstreuer Nicole, Tropsha Alexander, Muratov Eugene N
机构信息
UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27711, USA.
出版信息
Environ Health Perspect. 2025 May 19. doi: 10.1289/EHP15307.
BACKGROUND
Medication use among pregnant women is common, yet the safety of these medications for the developing fetus/baby is widely understudied. Quantitative Structure-Activity Relationship (QSAR) models can be used to predict the overall and trimester-specific developmental toxicity potential of chemicals, supporting the development of safer medications for pregnant women and regulatory assessment aligned with the 3Rs (efining, educing, and eplacing) of animal testing.
OBJECTIVES
This study aimed to collect and curate a database of compounds classified according to their developmental toxicity potential, use this database to develop and validate QSAR models for predicting prenatal developmental toxicity, and implement models via a user-friendly online platform to support regulatory assessments of drug candidates.
METHODS
We compiled and curated data from the FDA and Teratogen Information System (TERIS) databases and validated annotations with rigorous literature searches. The database was leveraged to create QSAR models using machine learning algorithms (RF, SVM, LightGBM) with Bayesian hyperparameter optimization. These models were implemented into a web tool.
RESULTS
We built a binary classification QSAR model for overall pregnancy risk, and separate QSAR models for trimester-specific risk, exhibiting correct classification rates of and 76% (overall), 80% (1 trimester), 95% (2 trimester), and 95% (3 trimester). Models showed a sensitivity between 53% and 90%, specificity between 46% and 100%, and coverage of 76% assessed using a five-fold external validation protocol. We established a publicly accessible web portal (https://detox.mml.unc.edu/) for developmental toxicity prediction of both overall and trimester-specific toxicity predictions.
CONCLUSIONS
DeTox can be employed to support regulatory assessment of pharmaceutical and cosmetic products aligned with the 3Rs of animal testing and to guide the development of safer drugs for pregnant populations. The curated dataset of developmental toxicants is publicly available, and all models are implemented in a public, user-friendly web tool, DeTox (velopmental icity), at https://detox.mml.unc.edu/. https://doi.org/10.1289/EHP15307.
背景
孕妇用药很常见，但这些药物对发育中的胎儿/婴儿的安全性尚未得到广泛研究。定量构效关系（QSAR）模型可用于预测化学物质的总体和特定孕期发育毒性潜力，支持为孕妇开发更安全的药物以及与动物试验的3R原则（替代、减少、优化）相一致的监管评估。
目的
本研究旨在收集和整理根据发育毒性潜力分类的化合物数据库，利用该数据库开发和验证用于预测产前发育毒性的QSAR模型，并通过用户友好的在线平台实施模型以支持对候选药物的监管评估。
方法
我们从美国食品药品监督管理局（FDA）和致畸剂信息系统（TERIS）数据库中汇编和整理数据，并通过严格的文献检索验证注释。利用该数据库，使用带有贝叶斯超参数优化的机器学习算法（随机森林、支持向量机、LightGBM）创建QSAR模型。这些模型被应用到一个网络工具中。
结果
我们建立了一个用于总体妊娠风险的二元分类QSAR模型，以及用于特定孕期风险的单独QSAR模型，总体妊娠风险模型的正确分类率为76%，特定孕期风险模型中，孕早期为80%，孕中期为95%，孕晚期为95%。使用五重外部验证方案评估，模型的灵敏度在53%至90%之间，特异性在46%至100%之间，覆盖率为76%。我们建立了一个公开可访问的门户网站（https://detox.mml.unc.edu/），用于总体和特定孕期毒性预测的发育毒性预测。
结论
DeTox可用于支持与动物试验的3R原则相一致的药品和化妆品的监管评估，并指导为孕妇开发更安全的药物。发育毒物的精选数据集是公开可用的，所有模型都在一个公开的、用户友好的网络工具DeTox（发育毒性）中实现，网址为https://detox.mml.unc.edu/。https://doi.org/10.1289/EHP15307。
Zotero 插件